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388 lines
11 KiB
C++
388 lines
11 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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// Copyright (C) 2018, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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#include "npy_blob.hpp"
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#include <opencv2/dnn/shape_utils.hpp>
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namespace opencv_test { namespace {
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template<typename TString>
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static std::string _tf(TString filename)
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{
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String rootFolder = "dnn/onnx/";
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return findDataFile(rootFolder + filename, false);
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}
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class Test_ONNX_layers : public DNNTestLayer
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{
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public:
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enum Extension
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{
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npy,
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pb
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};
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void testONNXModels(const String& basename, const Extension ext = npy, const double l1 = 0, const float lInf = 0)
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{
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String onnxmodel = _tf("models/" + basename + ".onnx");
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Mat inp, ref;
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if (ext == npy) {
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inp = blobFromNPY(_tf("data/input_" + basename + ".npy"));
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ref = blobFromNPY(_tf("data/output_" + basename + ".npy"));
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}
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else if (ext == pb) {
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inp = readTensorFromONNX(_tf("data/input_" + basename + ".pb"));
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ref = readTensorFromONNX(_tf("data/output_" + basename + ".pb"));
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}
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else
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CV_Error(Error::StsUnsupportedFormat, "Unsupported extension");
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checkBackend(&inp, &ref);
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Net net = readNetFromONNX(onnxmodel);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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net.setInput(inp);
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Mat out = net.forward();
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normAssert(ref, out, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
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}
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};
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TEST_P(Test_ONNX_layers, MaxPooling)
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{
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testONNXModels("maxpooling");
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testONNXModels("two_maxpooling");
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}
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TEST_P(Test_ONNX_layers, Convolution)
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{
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testONNXModels("convolution");
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testONNXModels("two_convolution");
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}
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TEST_P(Test_ONNX_layers, Deconvolution)
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{
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testONNXModels("deconvolution");
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testONNXModels("two_deconvolution");
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}
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TEST_P(Test_ONNX_layers, Dropout)
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{
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testONNXModels("dropout");
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}
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TEST_P(Test_ONNX_layers, Linear)
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{
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
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throw SkipTestException("");
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testONNXModels("linear");
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}
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TEST_P(Test_ONNX_layers, ReLU)
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{
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testONNXModels("ReLU");
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}
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TEST_P(Test_ONNX_layers, MaxPooling_Sigmoid)
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{
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testONNXModels("maxpooling_sigmoid");
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}
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TEST_P(Test_ONNX_layers, Concatenation)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD))
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throw SkipTestException("");
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testONNXModels("concatenation");
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}
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TEST_P(Test_ONNX_layers, AveragePooling)
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{
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testONNXModels("average_pooling");
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}
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TEST_P(Test_ONNX_layers, BatchNormalization)
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{
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testONNXModels("batch_norm");
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}
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TEST_P(Test_ONNX_layers, Transpose)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD))
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throw SkipTestException("");
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testONNXModels("transpose");
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}
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TEST_P(Test_ONNX_layers, Multiplication)
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{
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if ((backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) ||
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(backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD))
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throw SkipTestException("");
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testONNXModels("mul");
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}
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TEST_P(Test_ONNX_layers, Constant)
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{
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testONNXModels("constant");
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}
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TEST_P(Test_ONNX_layers, Padding)
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{
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testONNXModels("padding");
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}
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TEST_P(Test_ONNX_layers, MultyInputs)
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{
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const String model = _tf("models/multy_inputs.onnx");
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Net net = readNetFromONNX(model);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat inp1 = blobFromNPY(_tf("data/input_multy_inputs_0.npy"));
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Mat inp2 = blobFromNPY(_tf("data/input_multy_inputs_1.npy"));
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Mat ref = blobFromNPY(_tf("data/output_multy_inputs.npy"));
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checkBackend(&inp1, &ref);
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net.setInput(inp1, "0");
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net.setInput(inp2, "1");
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Mat out = net.forward();
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normAssert(ref, out, "", default_l1, default_lInf);
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}
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TEST_P(Test_ONNX_layers, DynamicReshape)
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{
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testONNXModels("dynamic_reshape");
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}
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INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_ONNX_layers, dnnBackendsAndTargets());
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class Test_ONNX_nets : public Test_ONNX_layers {};
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TEST_P(Test_ONNX_nets, Alexnet)
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{
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const String model = _tf("models/alexnet.onnx");
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Net net = readNetFromONNX(model);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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Mat inp = imread(_tf("../grace_hopper_227.png"));
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Mat ref = blobFromNPY(_tf("../caffe_alexnet_prob.npy"));
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checkBackend(&inp, &ref);
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net.setInput(blobFromImage(inp, 1.0f, Size(227, 227), Scalar(), false));
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ASSERT_FALSE(net.empty());
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Mat out = net.forward();
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normAssert(out, ref, "", default_l1, default_lInf);
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}
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TEST_P(Test_ONNX_nets, Squeezenet)
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{
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testONNXModels("squeezenet", pb);
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}
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TEST_P(Test_ONNX_nets, Googlenet)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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const String model = _tf("models/googlenet.onnx");
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Net net = readNetFromONNX(model);
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ASSERT_FALSE(net.empty());
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net.setPreferableBackend(backend);
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net.setPreferableTarget(target);
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std::vector<Mat> images;
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images.push_back( imread(_tf("../googlenet_0.png")) );
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images.push_back( imread(_tf("../googlenet_1.png")) );
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Mat inp = blobFromImages(images, 1.0f, Size(), Scalar(), false);
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Mat ref = blobFromNPY(_tf("../googlenet_prob.npy"));
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checkBackend(&inp, &ref);
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net.setInput(inp);
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ASSERT_FALSE(net.empty());
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Mat out = net.forward();
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normAssert(ref, out, "", default_l1, default_lInf);
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}
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TEST_P(Test_ONNX_nets, CaffeNet)
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{
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testONNXModels("caffenet", pb);
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}
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TEST_P(Test_ONNX_nets, RCNN_ILSVRC13)
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{
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testONNXModels("rcnn_ilsvrc13", pb);
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}
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#ifdef OPENCV_32BIT_CONFIGURATION
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TEST_P(Test_ONNX_nets, DISABLED_VGG16) // memory usage >2Gb
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#else
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TEST_P(Test_ONNX_nets, VGG16)
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#endif
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{
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double l1 = default_l1;
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double lInf = default_lInf;
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// output range: [-69; 72]
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) {
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l1 = 0.087;
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lInf = 0.585;
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}
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) {
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lInf = 1.2e-4;
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}
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testONNXModels("vgg16", pb, l1, lInf);
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}
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#ifdef OPENCV_32BIT_CONFIGURATION
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TEST_P(Test_ONNX_nets, DISABLED_VGG16_bn) // memory usage >2Gb
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#else
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TEST_P(Test_ONNX_nets, VGG16_bn)
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#endif
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{
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double l1 = default_l1;
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double lInf = default_lInf;
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// output range: [-16; 27]
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) {
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l1 = 0.0086;
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lInf = 0.037;
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}
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else if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)) {
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l1 = 0.031;
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lInf = 0.2;
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}
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testONNXModels("vgg16-bn", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, ZFNet)
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{
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testONNXModels("zfnet512", pb);
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}
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TEST_P(Test_ONNX_nets, ResNet18v1)
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{
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// output range: [-16; 22]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.022 : default_l1;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.12 : default_lInf;
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testONNXModels("resnet18v1", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, ResNet50v1)
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{
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// output range: [-67; 75]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.6 : 1.25e-5;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.51 : 1.2e-4;
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testONNXModels("resnet50v1", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
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{
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if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL
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|| target == DNN_TARGET_MYRIAD) {
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throw SkipTestException("");
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}
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testONNXModels("resnet101_duc_hdc", pb);
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}
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TEST_P(Test_ONNX_nets, TinyYolov2)
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{
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if (cvtest::skipUnstableTests ||
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(backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))) {
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throw SkipTestException("");
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}
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// output range: [-11; 8]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.017 : default_l1;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.14 : default_lInf;
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testONNXModels("tiny_yolo2", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, CNN_MNIST)
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{
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// output range: [-1952; 6574]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 3.82 : 4.4e-4;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 13.5 : 2e-3;
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testONNXModels("cnn_mnist", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, MobileNet_v2)
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{
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// output range: [-166; 317]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.38 : 7e-5;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2.87 : 5e-4;
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testONNXModels("mobilenetv2", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, LResNet100E_IR)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD))
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throw SkipTestException("");
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double l1 = default_l1;
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double lInf = default_lInf;
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// output range: [-3; 3]
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if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16) {
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l1 = 0.009;
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lInf = 0.035;
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}
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testONNXModels("LResNet100E_IR", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, Emotion_ferplus)
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{
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testONNXModels("emotion_ferplus", pb);
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}
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TEST_P(Test_ONNX_nets, Inception_v2)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE)
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throw SkipTestException("");
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testONNXModels("inception_v2", pb);
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}
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TEST_P(Test_ONNX_nets, DenseNet121)
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{
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// output range: [-87; 138]
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const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.12 : 2.2e-5;
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const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.74 : 1.23e-4;
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testONNXModels("densenet121", pb, l1, lInf);
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}
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TEST_P(Test_ONNX_nets, Inception_v1)
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{
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testONNXModels("inception_v1", pb);
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}
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TEST_P(Test_ONNX_nets, Shufflenet)
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{
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if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
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(target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_OPENCL || target == DNN_TARGET_MYRIAD))
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throw SkipTestException("");
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testONNXModels("shufflenet", pb);
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}
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INSTANTIATE_TEST_CASE_P(/**/, Test_ONNX_nets, dnnBackendsAndTargets());
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}} // namespace
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